| Soil carbon pool is the largest carbon pool in the terrestrial ecosystem, and its small changes have a great impact on the atmospheric carbon pool. As a main pathway of carbon emission from soils to the atmosphere, Soil respiration transfers the CO2 absorbed by plants photosynthesis into the atmosphere, and it plays a crucial role in the regulation of atmospheric CO2 concentration and global climate dynamics. However, due to the comprehensive cross-effect of environmental factors, phy-chemical factors and soil factors on soil respiration, the spatial variation of soil respiration varies at different degrees. Because of the spatial variation of soil respiration, it becomes difficult to estimate the soil CO2 emissions, especially in the mountain areas. In order to clearly understand the regional and global carbon budget, and comprehend the effect of soil respiration on the ecosystem carbon cycle, it is necessary to strengthen studies for the spatial heterogeneity of soil respiration, especially in the large heterogeneity regions.In this study,31 different sites were selected for study in Pangquangou Nature Reserve, and soil respiration (Rs) was measured using LI-COR-6400 portable photosynthesis system connected to a LI-COR 6400-09 soil chamber in September,2013. We also made the measurements of affecting factors of soil respiration including elevation (ELE), soil temperature (T), soil water content (SWC), soil bulk destiny (BD), soil total carbon (C), soil total nitrogen (N), soil alkali-hydrolyzable nitrogen (AHN), soil litter carbon (LC), litter nitrogen (LN), litter thickness (Lthi). Both traditional statistical methods and geostatistical methods were used to analyze the spatial heterogeneity and influencing factors of soil respiration. The objectives of this study were:1) to examine the spatial heterogeneity and spatial autocorrelation of soil respiration; 2) to find out key factors influencing the spatial variability on soil respiration; 3) to stimulate spatial distribution of soil respiration based on different statistical interpolation methods. The main results of this study were as follows:(1) The highest value of Rs occurred in larch plantation with the value of 9.15μ mol·m-2·s-1, and the lowest value was 2.52μ mol·m-2·s-1 in the meadow. As a whole, the coefficient of variation of Rs was 25.22%, showing moderate variability. The values of Rs were higher in low altitude areas and lower in high altitude areas. The lowest value of Rs among 6 vegetation type groups appeared in the meadow and the highest was in the leave-mixed forest. The spatial trend of Rs was roughly consistent with soil temperature.(2) Simple correlation analysis showed that the relationship of Rs in September with both T5, T5, T10 (p< 0.01) and BD (p< 0.01) was highly positively significant. Rs was highly negatively significant correlated with SWC30, N, C. The relationship between soil respiration and Lc, LN and Lthi was not significant (p> 0.05).(3) Regression analysis showed th at the fitting equations between Rs and T5, T10, T15, respectively explained for 41%,51%,62% of the spatial heterogeneity of Rs. The fitting equations between Rs and elevation, SWC30, BD30, N30, C30 explained for 68%,24%,26%,49%,38% of the spatial heterogeneity of Rs. The heterogeneity of Rs was controlled not only by environmental factors but also by phy-chemical factors and soil factors. The double exponential equations including T15 and N3o, T15 and C30 accounted for 68% and 65% of spatial variability of Rs.(4) The results from geostatistical analysis showed that linear model could reflect the spatial characteristics of Rs and its influencing factors except the AHN. The nugget/sill (Co/Co+C) value for Rs was 17.28%, indicating that the spatial heterogeneity of Rs was resulted mainly from structural factors. The spatial variability of Rs had strong spatial dependence on scale. The range of semi-variogram function showed the same ecological processes between Rs and T15, SWC30, BD30, C30. The required sampling numbers for Rs, T15, SWC30, BD30, N30 and C30 within ± 10%, ± 20% and± 30% of its actual mean at the 95% and 90% confidence level were 8,8,13,9,16, 14 and 7,6,9,6,11,10, respectively.(5) The spatial distribution of Rs using inverse distance weighting method (IDW), ordinary kriging (OK) and co-kriging interpolation method (COK) showed that high values of Rs occurred in southeastern and northwestern in the area, and low values appeared in the northeast of the Nature Reserve. Co-kriging method could accurately simulate the spatial distribution of Rs of Pangquangou Nature Reserve. |
PDF Full Text Request